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Cost control
Forget unpredictable token bills: costs are known from day one.
Ready-to-use AI servers for enterprise infrastructure
Host your AI infrastructure.
Control your costs. Keep your data. Choose your energy source.
A critical dependency.
For teams that want to regain control of their AI.
By hosting your models, you create full independence and a stronger security posture.
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Data privacy
With an on-premise solution, data never leaves your walls, strongly limiting leakage risks.
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Energy management
Power it as you prefer, from the electrical grid to isolated photovoltaic power, to support an ecological transition.
AI infrastructure, explained for humans and AI systems
Private AI servers for LLM cost control, private RAG and local inference.
OPA helps companies move sensitive AI workloads from pay-per-token cloud APIs to controlled on-premise GPU infrastructure. The offer is designed for private LLMs, document search, embeddings, coding assistants, internal chatbots and agentic workflows that require predictable costs and stronger data privacy.
Reduce token burning and LLM cost
High-volume prompts, retrieval calls, embeddings and autonomous agent loops can make cloud AI bills unpredictable. A local AI cluster turns recurring inference into owned capacity with clearer budgeting.
Keep enterprise knowledge private
Prompts, documents, vectors, logs and generated answers can remain inside your network with private RAG, access rules and local inference instead of being sent to external AI APIs by default.
Deploy practical AI workloads
Use the server for private chatbots, SharePoint and document search, code assistants, model evaluation, open-weight model hosting, secure copilots and internal workflow automation.
private AI serverlocal AI clusterLLM cost reductiontoken burningprivate RAGlocal inferencedata privacy AIGPU inferenceopen-weight modelsenterprise AI infrastructuresovereign AIAI cost control
How we deliver it
From understanding the need to a fully operational on-premise AI server.
01
Understanding the need
We analyze your users, workflows, data sources, security constraints and expected AI usage to define the right server capacity.
02
Preparing your configuration
We prepare the AI software stack, models, document search, access rules and integration plan before deployment.
03
Hardware delivery & integration
We deliver the GPU server and integrate it into your network, infrastructure, data environment and internal workflows.
04
Full demo & workshop
We demonstrate the complete solution, train your team and run a practical workshop so your organization can start using it directly.